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Thin Film Composite and/or Thin Film Nanocomposite Hollow Fiber Membrane for Water Treatment, Pervaporation and Gas/Vapor Separation

2018· preprint· en· W3121694316 on OpenAlexaff
Kailsh Khulbe, Takeshi Matsuura

Bibliographic record

VenuePreprints.org · 2018
Typepreprint
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPervaporationMaterials scienceMembraneWater vaporChemical engineeringComposite numberGas separationThin-film composite membraneThin filmNanocompositeFiberSubstrate (aquarium)NanotechnologyComposite materialOrganic chemistryChemistryEngineeringPermeation

Abstract

fetched live from OpenAlex

Nowadays asymmetric thin film composite (TFC) polymeric hollow fiber (HF) membranes are extensively used in industrial gas/vapor separations, water treatment etc. There are numerous advantages to use hollow fibers such as: low energy requirements, simplicity of operation, and high specificity. In the present article we discuss the progress made during the past decade in the preparation of the HF substrate and preparation/modification of the thin selective layer. Their applications in water treatment, dehydration of alcohols via pervaporation and gas/vapor separation are also demonstrated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.316
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2018
Admission routes1
Has abstractyes

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